Quantitative detection of dopamine, serotonin and their metabolites in rat model of Parkinson's disease using HPLC-MS/MS

Parkinson's disease (PD) is a common neurodegenerative disorder in aging and loss of dopamine (DA) in nigrostriatal system is an important character of PD. Animal models is very useful in the research of pathogenesis of PD. However, the DA content is not always decreased in the nigrostriatal system of these models as the situation of PD patients. Viral vector-based PD models in animals were newly developed technologies in recent years, and these models showed progressive neurodegenerative in nigrostriatal system and behavioural changes. As the other models, the DA conent of this model is also complex among different researchers. In this study, rAAV was used to transfer gene to the rat brain. Three different viral vectors consist of EGFP, α-synuclein wild type (WT) and α-synuclein A53T mutation was injected to the substantia nigra pars compacta (SNpc) to established the EGFP or α-synuclein over-expression models. After 16 weeks, the protein expression was determined by immunofluorescence and the loss of neurons in nigrostriatal system was detected by immunohistochemistry. The results showed that loss of dopaminergic neurons with positive correlation to the expression of α-synuclein, and the loss is even serious in the α-synuclein A53T group. It is indicated that the PD models were established successfully. HPLC-MS/MS was used to detect the neurotransmitter in the nigrostriatal system of these rat models of PD. The samples were separated by HS-F5 column, then detect in MS/MS using MRM mode which can analysis all of the neurotransmitters consist of DA, serotonin (5-HT) and their metabolites in one injection. The results showed the DA was decreased with its metabolites were increased whereas the 5-HT and its metabolite were increased. The results showed the neurotransmitter metabolism was disorder in rat models of PD. Our study focused on the neurotransmitter metabolism of rat model of PD, and it may provide a useful understanding of these models.